Abstract
Resource management in cloud datacenters is one of the most important issues for cloud service providers because it directly affects their profit. Energy and performance guarantee are two major concern of it. In energy aspect, the total estimated energy bill of datacenters is $11.5 billion and their energy bills double every five years [1, 2] Also, in performance guarantee aspect, many researches insist that performance metrics such as throughput and response time should be considered as well as availability in IaaS SLA [3, 4].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prediction. Available: http://searchstorage.techtarget.com.au/articles/28102-Predictions-2-9-Symantec-s-Craig-Scroggie
R. Buyya, A. Beloglazov, J. Abawajy, Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges (2010)
S.A. Baset, Cloud SLAs: present and future. SIGOPS Oper. Syst. Rev. 46(2), 57–66 (2012)
J. M. Myerson, Best Practices to Develop Slas for Cloud Computing. (IBM Corporation, New York, 2013) p. 9
A. Shankar U. Bellur, Virtual Machine Placement in Computing Clouds CoRR, abs/1011.5064 (2010)
M. Lin, A. Wierman, L.L.H. Andrew, E. Thereska, Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans. Netw. 21(5), 1378–1391 (2013)
Z. Xiao, W. Song, Q. Chen, Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107–1117 (2013)
Y. Koh, R. Knauerhase, P. Brett, M. Bowman, Z. Wen, C. Pu, An analysis of performance interference effects in virtual environments. IEEE Int. Symp. Perform Anal. Syst. Softw. 200–209 (2007)
C.D. Patel, A.J. Shah, Cost model for planning, development and operation of a data center. Development 107, 1–36 (2005)
W.-J. Kim, D.-K. Kang, S.-H. Kim, C.-H. Youn, Cost adaptive vm management for scientific workflow application in mobile cloud. Mob. Netw. Appl. 20(3), 328–336 (2015)
K. Hoste, A. Phansalkar, L. Eeckhout, A. Georges, L. K. John, K. De Bosschere, Performance prediction based on inherent program similarity PACT, vol 9 (Seattle, washinton 2006), p. 114
Memcoder. Available: https://linux.die.net/man/1/mencoder
Eucalyptus. Available: http://www.eucalyptus.com
K. Hoste, L. Eeckhout, Microarchitecture-independent workload characterization. IEEE Micro 27(3), 63–72 (2007)
A. Ali-Eldin, J. Tordsson, E. Elmroth, M. Kihl, Workload Classification for Efficient Auto-Scaling of Cloud Resources. (2005)
OpenStack. Available: http://www.openstack.org/
D.-K. Kang, F. Al-Hazemi, S.-H. Kim, M. Chen, L. Peng, C.-H. Youn, Adaptive VM management with two phase power consumption cost models in cloud datacenter. Mob. Netw. Appl. 21(5), 793–805 (2016)
M. Chen, Y. Zhang, L. Hu, T. Taleb, Z. Sheng, Cloud-based wireless network: virtualized, reconfigurable, smart wireless network to enable 5G technologies. Mob. Netw. Appl. 20(6), 704–712 (2015)
M. Chen, H. Jin, Y. Wen, V. Leung, Enabling technologies for future data center networking: a primer. IEEE Netw. 27(4), 8–15 (2013)
F. Xu, F. Liu, L. Liu, H. Jin, B.B. Li, B.B. Li, iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans. Comput. 63(12), 3012–3025 (2014)
D. Gupta, L. Cherkasova, R. Gardner, A. Vahdat, Enforcing performance isolation across virtual machines in xen, Proceedings 7th ACM/IFIP/USENIX international conference middleware, pp. 342–362, (2006)
A. Nisar, W.K. Liao, A. Choudhary, Scaling parallel I/O performance through I/O delegate and caching system, 2008 SC—International conference for high performance computing (Storage and Analysis, SC, Networking, 2008)
M. Chen, Y. Zhang, Y. Li, S. Mao, V.C.M. Leung, EMC: Emotion-aware mobile cloud computing in 5G. IEEE Netw. 29(2), 32–38 (2015)
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
YOCTO-WATT. Available: http://www.yoctopuce.com/EN/products/usb-electrical-sensors/yocto-watt
G-Technology. Available: http://www.g-technology.com/products/g-drive
PowerWake. Available: http://manpages.ubuntu.com/manpages/utopic/man1/powerwake.1.html
Montage. Available: http://montage.ipac.caltech.edu/
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Youn, CH., Chen, M., Dazzi, P. (2017). VM Placement via Resource Brokers in a Cloud Datacenter. In: Cloud Broker and Cloudlet for Workflow Scheduling. KAIST Research Series. Springer, Singapore. https://doi.org/10.1007/978-981-10-5071-8_2
Download citation
DOI: https://doi.org/10.1007/978-981-10-5071-8_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5070-1
Online ISBN: 978-981-10-5071-8
eBook Packages: Computer ScienceComputer Science (R0)